Data, Not Models, Is the Bottleneck for Physical AI
Language models learned from an internet-scale corpus of human knowledge. Physical AI has no equivalent dataset. The next generation of capable robots will depend on collecting vastly more high-quality, real-world interaction data.
Why We Instrument Humans Instead of Teleoperating Robots
If humans already perform physical tasks naturally, why force them to operate robots to generate training data? Instrumenting people allows data collection to happen at the speed, scale, and diversity of real human behavior.
The Fidelity Floor: What Manipulation Data Must Preserve
Not all motion data is useful for robot learning. Manipulation datasets must preserve the physical signals that define how an action actually happens—from finger articulation to precise 6-DoF trajectories and contact events.
Distribution Beats Volume: Capturing Beyond the Lab
A thousand demonstrations in one controlled environment can teach less than a smaller dataset collected across diverse real-world conditions. Physical AI needs distribution, not just volume.
From Human Demonstrations to Machine-Ready Physical Intelligence
Human behavior is one of the richest sources of physical intelligence. The challenge is turning natural demonstrations into synchronized, structured, machine-ready datasets that robotics models can learn from.
The Physical AI Data Stack: From Capture to Robot Learning
Building capable physical AI requires more than sensors and models. A scalable data stack must connect human behavior, multimodal capture, synchronization, validation, and machine-learning infrastructure.